The hardest part of FPGA-GPP heterogeneous design is not implementation -- it is knowing which tasks belong on the FPGA in the first place. Gen-TAS uses an LLM grounded in historical implementation data via RAG to generate explainable partitioning strategies, then connects those selections to a deterministic backend that produces reproducible FPGA SoC implementations. On CNN workloads the result is a 2.45x speedup; on SDR workloads, 92.53x, both relative to all-GPP baselines.
The constraint being removed is expert gatekeeping. FPGA-GPP partitioning today requires engineers who understand both application data flow and hardware resource tradeoffs simultaneously -- a combination that is rare and expensive. A team that cannot find that person either over-provisions compute on the GPP side or skips the FPGA entirely. Gen-TAS turns partitioning into a reproducible, documented, human-reviewable decision backed by historical implementation records rather than tribal knowledge.
The 92x SDR number is the more revealing one. SDR workloads are heavily bandwidth-constrained, and current practice assigns them to FPGA empirically because experienced engineers know it works. Gen-TAS gets to the same allocation from first principles using the implementation history. That is the shape of what LLM-aided EDA tooling actually means in practice: not generating RTL, but compressing the decision-making phase that currently burns senior engineer time before a line of RTL exists. Within 18 months this pattern -- LLM plus implementation history plus deterministic backend -- will be the default entry point for FPGA-GPP task allocation in any tool targeting embedded AI SoC design. Teams waiting for their EDA vendor to ship a GUI version will be two years behind.